# Achieving Peak Performance for Large Language Models: A Systematic Review

**Authors:** Zhyar Rzgar K Rostam, Sándor Szénási, Gábor Kertész  
**arXiv:** [2409.04833](https://arxiv.org/abs/2409.04833)  
**Date:** 2024-09-07  
**Summary page:** https://www.summarizepaper.com/en/paper/2409.04833v1/

## Key points

- Large language models (LLMs) in natural language processing (NLP) have gained attention for their impressive performance but come with high computational and memory costs as they scale up.
- Two primary approaches to optimize LLM performance: fine-tuning pre-trained models for specific tasks and finding ways to reduce costs or enhance training efficiency without compromising quality.
- A systematic literature review analyzed 65 publications out of 983 from 2017 to December 2023, focusing on methods to optimize and accelerate LLMs while maintaining cutting-edge outcomes.
- The study categorizes strategies into three classes: LLM training, LLM inference, and system serving, with a detailed taxonomy of recent optimization techniques like training optimization, hardware enhancements, scalability improvements, and reliability considerations.
- The paper concludes with a comprehensive comparison of each strategy class and presents two case studies demonstrating practical approaches to optimizing model training procedures and boosting inference efficiency within resource limitations.

## Summary

In the realm of natural language processing (NLP), large language models (LLMs) have garnered significant attention and success in recent years. These LLMs, with their impressive performance, rely on an extensive number of parameters to achieve optimal results. However, as these models scale up to the trillion-parameter range, the associated computational and memory costs escalate substantially. This poses a challenge for many researchers who may not have access to the resources required for training or applying such advanced models. To address this issue and optimize LLM performance, two primary approaches are commonly employed: fine-tuning pre-trained models for specific tasks to achieve state-of-the-art results, and finding ways to reduce costs or enhance training efficiency without compromising performance quality. In a systematic literature review (SLR) adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 65 publications out of 983 spanning from 2017 to December 2023 were meticulously analyzed across five databases. The study delves into various methods aimed at optimizing and accelerating LLMs while maintaining cutting-edge outcomes without sacrificing accuracy. It commences with an overview of the evolution of language modeling, followed by an intricate exploration of commonly utilized frameworks and libraries. A taxonomy is introduced categorizing strategies for improving and expediting LLMs into three classes: LLM training, LLM inference, and system serving. Furthermore, recent optimization and acceleration techniques such as training optimization, hardware enhancements, scalability improvements, and reliability considerations are thoroughly examined alongside a detailed taxonomy categorizing these strategies. The paper culminates in a comprehensive comparison of each class and strategy presented within it. Additionally, two insightful case studies are provided that showcase practical approaches towards addressing resource limitations inherent in working with LLMs while simultaneously enhancing overall performance levels. These case studies offer valuable insights into optimizing model training procedures and boosting inference efficiency effectively within the constraints posed by limited resources.